Inertial navigation systems are used in civil and military aviation, missiles and other projectiles, submarines and other underwater vehicles, space vehicles, as well as in numerous other vehicle applications. A typical inertial navigation system (INS) includes an inertial measurement unit (IMU) combined with computer control mechanisms, allowing the path of a vehicle to be controlled. An IMU includes inertial sensors or instruments such as accelerometers and/or gyroscopes for detecting and measuring inertial motion and acceleration in multiple directions.
Navigation systems usually model instrument errors and as such can track any long term drift in the instruments characteristics. On IMUs that may not be tied to any navigation, but only generate rates and acceleration, there is no navigation error model of the instruments that can be used to eliminate any long term drift. In order to overcome bias shifts resulting from aging in the environment, many inertial sensors employ a sensor model and calibration process that is independent of the actual operating environment. Velocity differences and angle differences generated by the inertial sensors are typically processed by a Kalman filter to estimate errors in the IMU including any gyroscope and/or accelerometer bias errors.
In conventional approaches, when multiple IMUs are used on a single platform such as an aircraft, each IMU has navigation mechanizations and Kalman filters that are used to transfer align to a master navigation system. During this process, the instrument errors of the respective IMUs are estimated as part of the transfer alignment Kalman filter. Providing each IMU with it own sensor model and calibration process is complex and costly when the IMU is not used as a navigation system, but only for rates and accelerations for a flight control system.
Accordingly, there is a need for improved inertial sensor calibration techniques that overcome the above deficiencies.